1. Sales Analysis: Impact of Product Price Change December, 2014 1

2. Objectives Identify the impact of product price change on its sales The impact is estimated by: – Forming two segments of customers: customers that took advantage of a Special Offer customers that did not take advantage of a Special Offer – Applying Intervention Time Series Analysis, Robust and Segmented Regression to each segment of customers 2

3. Customers that took advantage of special offer Have no obvious global trend in weekly sales data – Pre-Price Intervention Period: no significant linear trend – Special Order Period: there is a significant positive linear trend, slope=736 units/day – Post-Price Increase Period: significant positive linear trend, slope=127 units/day Price Increase has significant negative impact on sales 3

4. Customers that took advantage of special offer (Cont.) Special Offer has significant positive impact on sales – Number of stocking customers has a maximum of 4,235 in Special Offer Period In Pre Price Intervention Period the number of stocking customers was just 3% smaller then the maximum In Post Price Increase Period the number of stocking customers was 27% smaller then the maximum – Average number of units has a maximum of 77.55 units per customer in Special Offer Period In Pre Price Intervention Period the average units was 35% smaller then the maximum In Post Price Increase Period the average units was 23% smaller then the maximum – Total number of units per week has a maximum of 86,060 units per week in Special Offer Period In Pre Price Intervention Period the total units per week was 38% smaller then the maximum In Post Price Increase Period the total units per week was 56% smaller then the maximum 4

5. Customers that did not take advantage of special offer Have significant negative trend in weekly sales data (number of units sold) Have significant negative trend in number of stocking customers Price Change (Special Offer and Price Increase) do not affect average number of units 5

6. Data Structure Time Frame: Aug2013 – May2014 – Number of weeks: 44 Number of purchasing customers: 33,091 Number of customers that took advantage of special offer: 4,235 Number of customers that did not take advantage of special offer: 28,856 6

7. Product Sales Statistics (in units) 7 For Non-Users of Special Offer average number of units is stable across all three periods. Average number of units had a strong maximum for Users of Special Offer during a Special Offer Period (77.55). This value become 23% smaller in Post period. NameStatistic Stocking customers Non-Users of Special Offer Users of Special Offer All Purchasers Pre-Price Intervention Period Average units per customer 16.4550.0624.68 Tot units per week 53,969.453,305.5107,274.9 Special Offer Period Average units per customer 15.7577.5531.12 Tot units per week 52,835.386,060.1138,895.5 Post Price Intervention Period Average units per customer 16.1859.9824.16 Tot units per week 45,635.237,652.48,3287.6

8. Trend: Linear (Holt) Exponential Smoothing of Product Units (Non-Users of a Special Offer) 8 There is an obvious negative trend in sales Weekly Units Week Aug2013 May2014

9. Intervention Time Series Analysis / ARIMA Modeling Intervention Time Series Analysis (ITSA) is an important method within ARIMA class of models for analyzing the effect of sudden events on time series data The acronym ARIMA stands for "Auto-Regressive Integrated Moving Average" ITSA has become a standard statistical method for assessing the impact of an intervention (usually a planned policy change) on a time series ARIMA models are the most general class of models for analysis and forecasting a time series. Type of Intervention: – Step: Intervention variable is zero before the specified date and equals one after the date 9

10. Weekly Product Sales (users of a special offer) 10 Weekly Sales Week Aug2013May2014

11. Break Point Analysis (Chow Test) Product Units: weekly dynamics from 01Aug2013 through 30May2014 (Users of a Special Offer) If there is a suspicion or knowledge of structural change (the underlying process is not the same across all observations), a special tool – Chow’s Breakpoint Test can help to identify the structural change in time series data The Chow test is divided the data into two sub-samples. It then estimates the same trend equation to see whether there significant differences in the estimated equations. A significant difference indicates a structural change in the relationship under consideration (mechanism of the time series generation is changed) Results: the data strongly support the hypothesis that the date 01JAN2014 (a special offer starting point) is a break point (p- value < 0.006) 11

12. Weekly Dynamics of Number of Stocking Customers Among Special Offer Users 12 Aug2013 May2014 Week Weekly Number of Stocking Customers

13. Weekly Dynamics of Number of Stocking Customers among Special Offer Non Users 13 There is a significant linear negative trend in Post period: the slope = -4.3 customers per day Aug2013May2014 Weekly Number of Stocking Customers Week